This is Part Two in an ongoing series of articles by weather guru Michael Carr. Click here to review Part One

Gathering sufficient weather dataand understanding itwill help you avoid unfriendly weather like this

What do comic strips, stock reports, and medical charts all have in common? They display information in a way that is easily scanned by the human eye and understood by the brain. To make any type of decision, we need information in sufficient quantity and quality, referenced in time and space, so that we can place ourselves in the data field to understand how that data will affect us.

Most problems encounted in understanding weather relate to gathering and displaying the information and not understanding weather dynamics or processes. Weather does not stand still and wait for us; it is constantly in motion, and we are always playing catch-up, looking at what has happened in the recent past and using known weather behavior patterns to predict the future.

So, what is the best method for understanding, analyzing, and predicting weather? Using weather products that contain a high density of data. For instance, Marine Prediction Center (MPC) weather charts show a vast quantity of data, including centers of high and low-pressure systems, their movement in six and 24-hour increments, location of fronts, central pressure and isobars, wind speed and direction, surface troughs, dry-lines, tropical weather features, and ocean-current boundaries.

Why is high density important? Because we make analyses and decisions through comparisonbig versus little, bright versus dark, hot versus cold, cloudy versus sunny. When more data is available, then more comparisons are possible and the better our decisions will be. But we need information displayed in the proper format, and here is the keythe human brain is very efficient at scanning, but not so efficient when it comes to looking at one item at a time. When you want to examine photographs, what do you do? You lay them out side by side on a table. Do you stack them in a pile? No, of course not. You lay things along side each other so you can compare them. You rearrange the order and sequence until you find the correct perspective.

Beginning with high-density data, like this 24-hour surface forecast, is the best method for understanding, analyzing, and predicting weather.

This method of decision-making applies directly to weather. In marine weather you must lay upper-air (500-mb) analyses and forecast charts side by side, in left-to-right sequencing. Begin with an analysis chart, followed by a 24-hour forecast chart, then a 48-hour forecast and a 96-hour forecast. Directly below the upper air charts, lay the surface charts, and directly below these lay out the sea-state charts. Now scan from left to right, top to bottom and note the flow of weather featurestroughs and ridges, lows and highs.

Then you can take this a step further with a variety of colors. Perhaps you can use a red marker to highlight the upper-air storm track path, a blue one to mark surface lows, and yellow to mark surface highs. Use blue arrows to trace movement of lows, and yellow to trace movement of highs. Now correlate troughs with lows and ridges with highs.

At this point, the weather should begin to take on motion. Patterns should begin to appear and you should be seeing weather moving through increments of time. The smaller the increments of time the better, since the human brain sees change through comparison and the more opportunities for comparison, the more easily change is appreciated. Watch a house being built every day and you see change, watch it every week or every month and you do not see the change as well as you see completion.

Are rainbow colors and fancy design necessary for understanding weather? No. What is needed are weather charts and images that show data, not "chart junk." Fancy color graphics, as well as three dimensions are often just a distraction from showing hard-core data. So always ask this question, "What percentage of this product is data and what percentage is presentation?" You want data, not fancy graphics.

In the process of forcing yourself to make visual comparisons you also need to demonstrate causality. What is causality? It is demonstrating what causes an event. Weather events do not occur mysteriously. There are no unpredictable rogue waves nor are there any unpredicted stormseven the conditions reported in The Perfect Storm were well predicted, days in advance. For every surface low-pressure system there exists an upper level trough, and for every surface high-pressure system there is a supporting ridge.

"Thus the key is to gather sufficient data so that you can articulate why events are occurring."

Thus the key is to gather sufficient datacharts, satellite images, text, and on-scene measurements such as wind speed and direction, pressure, swell direction and height so that you can articulate why events are occurring. Your conversation might sound like this: "We have an upper-level trough approaching the coast, and it shows signs of increasing in strength since the upper-level winds are gaining in strength, thus a forecast of a low near Cape Hatteras seems a reasonable expectation. Additionally, the Gulf Stream is showing temperatures in the high 80s, so moisture from the Stream will likely fuel a developing low, increasing the probability that it could develop to gale force."

You are seeing relationships and causality. If you are unable to visualize and show causality, the reasons are simpleyou either have insufficient data, inaccurate data, the data is displayed improperly, or the data is distorted.

By establishing data credibility early in a weather analysis process, you are able to gain high confidence over time. That is, if we know our initial data is correct and we have verified this by observing weather features and events, then we have a base upon which to build future analyses and forecasts. This is quality control, making sure you have good data now so that future decisions come from a base of high confidence. Weather follows patterns and conforms to the laws of physics, but since we are always playing catch-up with it, we depend on detecting relationships and establishing probabilities of events, not relying too much on predicting the particulars of an absolute, deterministic event.